nflData2022
Eric Chu
2023-09-27
The following code is used to generate the data plots.
Importing CSVs
nfl2022 <- read.csv("nfl2022.csv", header = TRUE)
driveAvg <- read.csv("driveAvg2022.csv", header = TRUE)
passOff <- read.csv("passOff2022.csv", header = TRUE)
rushOff <- read.csv("rushOff2022.csv", header = TRUE)
scoreOff <- read.csv("scoreOff2022.csv", header = TRUE)
"I added the abbrevation column to each dataframe, this will make plotting, sorting, and organization easier."## [1] "I added the abbrevation column to each dataframe, this will make plotting, sorting, and organization easier."nfl2022 <- nfl2022[order(nfl2022$Tm, decreasing = FALSE), ]
driveAvg <- driveAvg[order(driveAvg$Tm, decreasing = FALSE), ]
passOff <- passOff[order(passOff$Tm, decreasing = FALSE), ]
rushOff <- rushOff[order(rushOff$Tm, decreasing = FALSE), ]
scoreOff <- scoreOff[order(scoreOff$Tm, decreasing = FALSE), ]
"In order to have the stats properly correspond to each team, I decided to use alphabetical order since it is the most consistent ordering between each data frame."## [1] "In order to have the stats properly correspond to each team, I decided to use alphabetical order since it is the most consistent ordering between each data frame."head(nfl2022)## Tm Abv. W L T W.L. PF PA PD MoV SoS SRS OSRS DSRS
## 30 Arizona Cardinals ARI 4 13 0 0.235 340 449 -109 -6.4 0.2 -6.2 -1.9 -4.3
## 23 Atlanta Falcons ATL 7 10 0 0.412 365 386 -21 -1.2 -0.9 -2.1 -0.1 -2.0
## 8 Baltimore Ravens+ BAL 10 7 0 0.588 350 315 35 2.1 1.1 3.1 -0.2 3.4
## 3 Buffalo Bills* BUF 13 3 0 0.813 455 286 169 10.6 0.4 10.9 7.1 3.8
## 24 Carolina Panthers CAR 7 10 0 0.412 347 374 -27 -1.6 -0.6 -2.2 -1.3 -0.9
## 32 Chicago Bears CHI 3 14 0 0.176 326 463 -137 -8.1 1.6 -6.4 -2.5 -4.0head(driveAvg)## Rk Tm Abv. G X.Dr Plays Sc. TO. Plays.1 Yds Start Time
## 12 12 Arizona Cardinals ARI 17 187 1176 32.1 12.8 6.3 29.2 Own 26.8 2:45
## 32 32 Atlanta Falcons ATL 17 167 1042 39.5 11.4 6.2 32.3 Own 27.7 3:02
## 20 20 Baltimore Ravens BAL 17 180 1095 38.9 11.7 6.1 32.0 Own 29.6 2:57
## 31 31 Buffalo Bills BUF 16 171 1068 45.0 15.2 6.2 37.1 Own 30.0 2:47
## 14 14 Carolina Panthers CAR 17 187 1009 35.3 9.6 5.4 27.9 Own 28.4 2:36
## 18 18 Chicago Bears CHI 17 181 1020 34.3 12.7 5.6 28.7 Own 29.9 2:46
## Pts
## 12 1.65
## 32 2.00
## 20 1.90
## 31 2.52
## 14 1.76
## 18 1.85head(passOff)## X Tm Abv. G Cmp Att Cmp. Yds TD TD. Int Int. Lng Y.A AY.A
## 18 18 Arizona Cardinals ARI 17 433 664 65.2 3626 17 2.6 17 2.6 77 6.0 5.3
## 31 31 Atlanta Falcons ATL 17 257 415 61.9 2699 17 4.1 9 2.2 75 7.1 6.9
## 28 28 Baltimore Ravens BAL 17 300 488 61.5 3040 19 3.9 13 2.7 75 6.6 6.1
## 8 8 Buffalo Bills BUF 16 361 574 62.9 4129 35 6.1 14 2.4 98 7.5 7.6
## 29 29 Carolina Panthers CAR 17 267 457 58.4 2996 16 3.5 13 2.8 75 7.1 6.5
## 32 32 Chicago Bears CHI 17 223 377 59.2 2219 19 5.0 15 4.0 56 6.9 6.1
## Y.C Y.G Rate Sk Yds.1 Sk. NY.A ANY.A X4QC GWD EXP.
## 18 9.2 213.3 79.2 46 340 6.5 5.1 4.5 1 1 -33.98\\
## 31 11.4 158.8 87.7 37 228 8.2 6.0 5.8 3 4 28.90\\
## 28 10.7 178.8 82.5 38 162 7.2 5.8 5.4 2 3 6.19\\
## 8 11.9 258.1 95.8 33 162 5.4 6.8 6.9 3 4 129.14\\
## 29 12.2 176.2 80.2 36 250 7.3 6.1 5.5 0 1 -18.72\\
## 32 11.7 130.5 80.3 58 379 13.3 5.1 4.4 1 2 -64.19\\head(rushOff)## Rk Tm Abv. G Att Yds TD Lng Y.A Y.G Fmb EXP
## 1 22 Arizona Cardinals ARI 17 434 1873 15 45 4.3 110.2 25 16.79
## 2 3 Atlanta Falcons ATL 17 559 2718 17 44 4.9 159.9 21 46.17
## 3 2 Baltimore Ravens BAL 17 526 2720 14 79 5.2 160.0 21 48.10
## 4 9 Buffalo Bills BUF 16 430 2232 15 44 5.2 139.5 24 33.20
## 5 10 Carolina Panthers CAR 17 483 2210 16 60 4.6 130.0 29 26.76
## 6 1 Chicago Bears CHI 17 558 3014 18 67 5.4 177.3 33 69.64head(scoreOff)## Rk Tm Abv. G RshTD RecTD PR.TD KR.TD FblTD IntTD OthTD AllTD
## 31 31 Arizona Cardinals ARI 17 15 17 NA NA 2 3 NA 37
## 23 23 Atlanta Falcons ATL 17 17 17 NA 1 1 1 1 38
## 3 3 Baltimore Ravens BAL 17 14 19 NA 1 NA NA NA 34
## 2 2 Buffalo Bills BUF 16 15 35 NA 2 NA 1 NA 53
## 19 19 Carolina Panthers CAR 17 16 16 NA NA 2 2 NA 36
## 32 32 Chicago Bears CHI 17 18 19 NA NA NA NA NA 37
## X2.00.PM X2PA D2P XPM XPA FGM FGA Sfty Pts Pts.G
## 31 5 9 NA 24 27 28 32 NA 340 20.0
## 23 3 3 NA 33 35 32 37 1 365 21.5
## 3 2 2 NA 31 32 37 43 NA 350 20.6
## 2 2 3 NA 48 50 27 31 2 455 28.4
## 19 1 4 NA 30 32 33 35 NA 347 20.4
## 32 1 5 NA 27 32 25 27 NA 326 19.2Passing Success and Offensive Success
# Passing Attempts vs Offensive Rank
plot(passOff$Att, scoreOff$Pts.G, type = "n", xlab = "Num. of Passing Atts.", ylab = "Points per Game")
text(passOff$Att, scoreOff$Pts.G, labels = passOff$Abv.)
cor(passOff$Att, scoreOff$Pts.G)## [1] 0.1469385# Passing TDs vs Offensive Rank
plot(passOff$TD, scoreOff$Pts.G, type = "n", xlab = "Num. of Passing TDs", ylab = "Points per Game")
text(passOff$TD, scoreOff$Pts.G, labels = passOff$Abv)
cor(passOff$TD, scoreOff$Pts.G)## [1] 0.8113075# Passing Yards vs Offensive Rank
plot(passOff$Yds, scoreOff$Pts.G, type = "n", xlab = "Num. of Passing YDs", ylab = "Points per Game")
text(passOff$Yds, scoreOff$Pts.G, labels = passOff$Abv)
cor(passOff$Yds, scoreOff$Pts.G)## [1] 0.5871392# Passing Y/A vs Offensive Rank
plot(passOff$Y.A, scoreOff$Pts.G, type = "n", xlab = "Passing Yards per Attempt", ylab = "Points per Game")
text(passOff$Y.A, scoreOff$Pts.G, labels = passOff$Abv)
cor(passOff$Y.A, scoreOff$Pts.G)## [1] 0.6993306# Passer Rating vs Offensive Rank
plot(passOff$Rate, scoreOff$Pts.G, type = "n", xlab = "Passer Rating", ylab = "Points per Game")
text(passOff$Rate, scoreOff$Pts.G, labels = passOff$Abv)
cor(passOff$Rate, scoreOff$Pts.G)## [1] 0.828569Rushing Success and Offensive Success
# Rushing Attempts vs Offensive Rank
plot(rushOff$Att, scoreOff$Pts.G, type = "n", xlab = "Num. of Rushing Atts.", ylab = "Points per Game")
text(rushOff$Att, scoreOff$Pts.G, labels = rushOff$Abv.)
cor(rushOff$Att, scoreOff$Pts.G)## [1] 0.03321405# Rushing TDs vs Offensive Rank
plot(rushOff$TD, scoreOff$Pts.G, type = "n", xlab = "Num. of Rushing TDs", ylab = "Points per Game")
text(rushOff$TD, scoreOff$Pts.G, labels = rushOff$Abv)
cor(rushOff$TD, scoreOff$Pts.G)## [1] 0.6077728# Rushing YDs vs Offensive Rank
plot(rushOff$Yds, scoreOff$Pts.G, type = "n", xlab = "Num. of Rushing Yds", ylab = "Points per Game")
text(rushOff$Yds, scoreOff$Pts.G, labels = rushOff$Abv)
cor(rushOff$Yds, scoreOff$Pts.G)## [1] 0.1730041# Rushing Y/A vs Offensive Rank
plot(rushOff$Y.A, scoreOff$Pts.G, type = "n", xlab = "Rushing Y/A", ylab = "Points per Game")
text(rushOff$Y.A, scoreOff$Pts.G, labels = rushOff$Abv.)
cor(rushOff$Y.A, scoreOff$Pts.G)## [1] 0.2996935Passing Success and Team Success
#Pass Attempts vs WL%
plot(passOff$Att, nfl2022$W.L., type = "n", xlab = "Num. of Pass Atts.", ylab = "Win-Loss Percentage")
text(passOff$Att, nfl2022$W.L., labels = passOff$Abv.)
'
plot(passOff$Att[passOff$Att < 600 & passOff$Att > 550 & nfl2022$W.L.< 0.55 & nfl2022$W.L. > 0.5], nfl2022$W.L.[passOff$Att < 600 & passOff$Att > 550 & nfl2022$W.L.< 0.55 & nfl2022$W.L. > 0.5]
, type = "n", xlab = "Pass Attempts", ylab = "Win-Loss Percentage")
text(passOff$Att, nfl2022$W.L., labels = passOff$Abv.)
'## [1] "\nplot(passOff$Att[passOff$Att < 600 & passOff$Att > 550 & nfl2022$W.L.< 0.55 & nfl2022$W.L. > 0.5], nfl2022$W.L.[passOff$Att < 600 & passOff$Att > 550 & nfl2022$W.L.< 0.55 & nfl2022$W.L. > 0.5]\n, type = \"n\", xlab = \"Pass Attempts\", ylab = \"Win-Loss Percentage\")\ntext(passOff$Att, nfl2022$W.L., labels = passOff$Abv.)\n"cor(passOff$Att, nfl2022$W.L.)## [1] 0.231374# Passing TDs vs WL%
plot(passOff$TD, nfl2022$W.L., type = "n", xlab = "Num. of Passing TDs", ylab = "Win-Loss Percentage")
text(passOff$TD, nfl2022$W.L., labels = passOff$Abv)
cor(passOff$TD, nfl2022$W.L.)## [1] 0.6893112# Passing Yards vs WL%
plot(passOff$Yds, nfl2022$W.L., type = "n", xlab = "Num. of Passing Yds", ylab = "Win-Loss Percentage")
text(passOff$Yds, nfl2022$W.L., labels = passOff$Abv)
cor(passOff$Yds, nfl2022$W.L.)## [1] 0.6079146# Passing Y/A vs WL%
plot(passOff$Y.A, nfl2022$W.L., type = "n", xlab = "Passing Y/A", ylab = "Win-Loss Percentage")
text(passOff$Y.A, nfl2022$W.L., labels = passOff$Abv)
cor(passOff$Y.A, nfl2022$W.L.)## [1] 0.5917355# Passer Rating vs WL%
plot(passOff$Rate, nfl2022$W.L., type = "n", xlab = "Passer Rating", ylab = "Win-Loss Percentage")
text(passOff$Rate,nfl2022$W.L., labels = passOff$Abv)
cor(passOff$Rate, nfl2022$W.L.)## [1] 0.7585994Rushing Success and Team Success
# Rushing Attempts vs WL%
plot(rushOff$Att, nfl2022$W.L., type = "n", xlab = "Num. of Rushing Atts.", ylab = "Win-Loss Percentage")
text(rushOff$Att, nfl2022$W.L., labels = rushOff$Abv.)
cor(rushOff$Att, nfl2022$W.L.)## [1] 0.02374545# Rushing TDs vs WL%
plot(rushOff$TD, nfl2022$W.L., type = "n", xlab = "Num. of Rushing TDs", ylab = "Win-Loss Percentage")
text(rushOff$TD, nfl2022$W.L., labels = rushOff$Abv)
cor(rushOff$TD, nfl2022$W.L.)## [1] 0.4989969# Rushing YDs vs WL%
plot(rushOff$Yds, nfl2022$W.L., type = "n", xlab = "Num. of Rushing Yds", ylab = "Win-Loss Percentage")
text(rushOff$Yds, nfl2022$W.L., labels = rushOff$Abv)
cor(rushOff$Yds, nfl2022$W.L.)## [1] 0.04876353# Rushing Y/A vs WL%
plot(rushOff$Y.A, nfl2022$W.L., type = "n", xlab = "Rushing Y/A", ylab = "Win-Loss Percentage")
text(rushOff$Y.A, nfl2022$W.L., labels = rushOff$Abv.)
cor(rushOff$Y.A, nfl2022$W.L.)## [1] 0.08316379Consider the Balance of Passing vs Rushing
'Note: The total TDs are strictly from the offense, so pick-sixes, punt return TDs, and so on will not count'## [1] "Note: The total TDs are strictly from the offense, so pick-sixes, punt return TDs, and so on will not count"RushTDsProp <- rushOff$TD/(rushOff$TD + passOff$TD)
RushTDsProp <- cbind(rushOff$Abv., RushTDsProp)
PassTDsProp <- passOff$TD/(rushOff$TD + passOff$TD)
PassTDsProp <- cbind(passOff$Abv., PassTDsProp)
RushYdsProp <- rushOff$Yds/(rushOff$Yds + passOff$Yds)
RushYdsProp <- cbind(rushOff$Abv., RushYdsProp)
PassYdsProp <- passOff$Yds/(rushOff$Yds + passOff$Yds)
PassYdsProp <- cbind(passOff$Abv., PassYdsProp)
"I'm sure there are better ways to combine the prortion of yards and TDs with team abbreviations, but this will do for now."## [1] "I'm sure there are better ways to combine the prortion of yards and TDs with team abbreviations, but this will do for now."#Show head of all the new data frames
print(RushTDsProp)## RushTDsProp
## [1,] "ARI" "0.46875"
## [2,] "ATL" "0.5"
## [3,] "BAL" "0.424242424242424"
## [4,] "BUF" "0.3"
## [5,] "CAR" "0.5"
## [6,] "CHI" "0.486486486486487"
## [7,] "CIN" "0.285714285714286"
## [8,] "CLE" "0.5"
## [9,] "DAL" "0.461538461538462"
## [10,] "DEN" "0.379310344827586"
## [11,] "DET" "0.442307692307692"
## [12,] "GB" "0.307692307692308"
## [13,] "HOU" "0.259259259259259"
## [14,] "IND" "0.32"
## [15,] "JAX" "0.390243902439024"
## [16,] "KC" "0.305084745762712"
## [17,] "LV" "0.3"
## [18,] "LAC" "0.365853658536585"
## [19,] "LAR" "0.483870967741935"
## [20,] "MIA" "0.285714285714286"
## [21,] "MIN" "0.375"
## [22,] "NE" "0.387096774193548"
## [23,] "NO" "0.333333333333333"
## [24,] "NYG" "0.552631578947368"
## [25,] "NYJ" "0.464285714285714"
## [26,] "PHI" "0.56140350877193"
## [27,] "PIT" "0.571428571428571"
## [28,] "SF" "0.4"
## [29,] "SEA" "0.285714285714286"
## [30,] "TB" "0.161290322580645"
## [31,] "TEN" "0.5"
## [32,] "WAS" "0.272727272727273"print(PassTDsProp)## PassTDsProp
## [1,] "ARI" "0.53125"
## [2,] "ATL" "0.5"
## [3,] "BAL" "0.575757575757576"
## [4,] "BUF" "0.7"
## [5,] "CAR" "0.5"
## [6,] "CHI" "0.513513513513513"
## [7,] "CIN" "0.714285714285714"
## [8,] "CLE" "0.5"
## [9,] "DAL" "0.538461538461538"
## [10,] "DEN" "0.620689655172414"
## [11,] "DET" "0.557692307692308"
## [12,] "GB" "0.692307692307692"
## [13,] "HOU" "0.740740740740741"
## [14,] "IND" "0.68"
## [15,] "JAX" "0.609756097560976"
## [16,] "KC" "0.694915254237288"
## [17,] "LV" "0.7"
## [18,] "LAC" "0.634146341463415"
## [19,] "LAR" "0.516129032258065"
## [20,] "MIA" "0.714285714285714"
## [21,] "MIN" "0.625"
## [22,] "NE" "0.612903225806452"
## [23,] "NO" "0.666666666666667"
## [24,] "NYG" "0.447368421052632"
## [25,] "NYJ" "0.535714285714286"
## [26,] "PHI" "0.43859649122807"
## [27,] "PIT" "0.428571428571429"
## [28,] "SF" "0.6"
## [29,] "SEA" "0.714285714285714"
## [30,] "TB" "0.838709677419355"
## [31,] "TEN" "0.5"
## [32,] "WAS" "0.727272727272727"print(RushYdsProp)## RushYdsProp
## [1,] "ARI" "0.340607383160575"
## [2,] "ATL" "0.501753738231493"
## [3,] "BAL" "0.472222222222222"
## [4,] "BUF" "0.350888225121836"
## [5,] "CAR" "0.424510180560891"
## [6,] "CHI" "0.575960252245366"
## [7,] "CIN" "0.264909847434119"
## [8,] "CLE" "0.419615773508595"
## [9,] "DAL" "0.380841895923102"
## [10,] "DEN" "0.350099511489054"
## [11,] "DET" "0.337306501547988"
## [12,] "GB" "0.367798085291558"
## [13,] "HOU" "0.306224066390042"
## [14,] "IND" "0.352208380520951"
## [15,] "JAX" "0.348312757201646"
## [16,] "KC" "0.280147895335609"
## [17,] "LV" "0.343567495411313"
## [18,] "LAC" "0.24950884086444"
## [19,] "LAR" "0.348291046340952"
## [20,] "MIA" "0.272067129256092"
## [21,] "MIN" "0.270301057770545"
## [22,] "NE" "0.338818249813014"
## [23,] "NO" "0.349312654212196"
## [24,] "NYG" "0.443798449612403"
## [25,] "NYJ" "0.311702717692734"
## [26,] "PHI" "0.379346840036287"
## [27,] "PIT" "0.37800875273523"
## [28,] "SF" "0.37966537966538"
## [29,] "SEA" "0.341700133868809"
## [30,] "TB" "0.221920597217509"
## [31,] "TEN" "0.422398414271556"
## [32,] "WAS" "0.381656277827248"print(PassYdsProp)## PassYdsProp
## [1,] "ARI" "0.659392616839425"
## [2,] "ATL" "0.498246261768507"
## [3,] "BAL" "0.527777777777778"
## [4,] "BUF" "0.649111774878164"
## [5,] "CAR" "0.575489819439109"
## [6,] "CHI" "0.424039747754634"
## [7,] "CIN" "0.735090152565881"
## [8,] "CLE" "0.580384226491405"
## [9,] "DAL" "0.619158104076898"
## [10,] "DEN" "0.649900488510946"
## [11,] "DET" "0.662693498452012"
## [12,] "GB" "0.632201914708442"
## [13,] "HOU" "0.693775933609958"
## [14,] "IND" "0.647791619479049"
## [15,] "JAX" "0.651687242798354"
## [16,] "KC" "0.719852104664391"
## [17,] "LV" "0.656432504588687"
## [18,] "LAC" "0.75049115913556"
## [19,] "LAR" "0.651708953659048"
## [20,] "MIA" "0.727932870743908"
## [21,] "MIN" "0.729698942229455"
## [22,] "NE" "0.661181750186986"
## [23,] "NO" "0.650687345787804"
## [24,] "NYG" "0.556201550387597"
## [25,] "NYJ" "0.688297282307266"
## [26,] "PHI" "0.620653159963713"
## [27,] "PIT" "0.62199124726477"
## [28,] "SF" "0.62033462033462"
## [29,] "SEA" "0.658299866131191"
## [30,] "TB" "0.778079402782491"
## [31,] "TEN" "0.577601585728444"
## [32,] "WAS" "0.618343722172752"Offensive Success Based on Passing and Rushing Proportions
#Passing Yds Prop vs Offensive Rank
plot(PassYdsProp[,2], scoreOff$Pts.G, type = "n", xlab = "Passing Yds Percentage", ylab = "Points per Game")
text(PassYdsProp[,2], scoreOff$Pts.G, labels = passOff$Abv.)
cor(as.numeric(PassYdsProp[,2]), scoreOff$Pts.G)## [1] 0.1886544#Passing TDs Prop vs Offensive Rank
plot(PassTDsProp[,2], scoreOff$Pts.G, type = "n", xlab = "Passing TDs Percentage", ylab = "Points per Game")
text(PassTDsProp[,2], scoreOff$Pts.G, labels = passOff$Abv.)
cor(as.numeric(PassTDsProp[,2]), scoreOff$Pts.G)## [1] 0.06443996#Rushing Yds Prop vs Offensive Rank
plot(RushYdsProp[,2], scoreOff$Pts.G, type = "n", xlab = "Rushing Yds Percentage", ylab = "Points per Game")
text(RushYdsProp[,2], scoreOff$Pts.G, labels = passOff$Abv.)
cor(as.numeric(RushYdsProp[,2]), scoreOff$Pts.G)## [1] -0.1886544# Rushing TDs Prop vs Offensive Rank
plot(RushTDsProp[,2], scoreOff$Pts.G, type = "n", xlab = "Rushing TDs Percentage", ylab = "Points per Game")
text(RushTDsProp[,2], scoreOff$Pts.G, labels = passOff$Abv.)
cor(as.numeric(RushTDsProp[,2]), scoreOff$Pts.G)## [1] -0.06443996"Because cbind typecasted the proportions to characters (due to the fact that we binded the values with abv. column which is a character vector)
I had to use the as.numeric function"## [1] "Because cbind typecasted the proportions to characters (due to the fact that we binded the values with abv. column which is a character vector) \nI had to use the as.numeric function"Team Success Based on Passing and Rushing Proportions
#Pasing Yds Prop vs Offensive Rank
plot(PassYdsProp[,2], nfl2022$W.L., type = "n", xlab = "Passing Yds Proportion", ylab = "Win/Loss Percentage")
text(PassYdsProp[,2], nfl2022$W.L., labels = passOff$Abv.)
cor(as.numeric(PassYdsProp[,2]), nfl2022$W.L.)## [1] 0.2805756#Passing TDs Prop vs Offensive Rank
plot(PassTDsProp[,2], nfl2022$W.L., type = "n", xlab = "Passing TDs Proportion", ylab = "Win/Loss Percentage")
text(PassTDsProp[,2], nfl2022$W.L., labels = passOff$Abv.)
cor(as.numeric(PassTDsProp[,2]), nfl2022$W.L.)## [1] 0.05787127#Rushing Yds Prop vs Offensive Rank
plot(RushYdsProp[,2], nfl2022$W.L., type = "n", xlab = "Rushing TDs Proportion", ylab = "Win/Loss Percentage")
text(RushYdsProp[,2], nfl2022$W.L., labels = passOff$Abv.)
cor(as.numeric(RushYdsProp[,2]), nfl2022$W.L.)## [1] -0.2805756# Rushing TDs Prop vs Offensive Rank
plot(RushTDsProp[,2], nfl2022$W.L., type = "n", xlab = "Rushing Yds Proportion", ylab = "Win/Loss Percentage")
text(RushTDsProp[,2], nfl2022$W.L., labels = passOff$Abv.)
cor(as.numeric(RushTDsProp[,2]), nfl2022$W.L.)## [1] -0.05787127Other Calculations
sd(rushOff$Yds)## [1] 395.3786sd(passOff$Yds)## [1] 614.6549sd(rushOff$TD)## [1] 5.295978sd(passOff$TD)## [1] 6.969692sd(passOff$Rate)## [1] 8.177772Passing Offense vs Rushing Y/A
plot(rushOff$Y.A, passOff$Rate, type = "n", xlab = "Rushing Y/A", ylab = "Passer Rating")
text(rushOff$Y.A, passOff$Rate, labels = rushOff$Abv.)
cor(rushOff$Y.A, passOff$Rate)## [1] 0.1253517plot(rushOff$Yds, passOff$Rate, type = "n", xlab = "Rushing Yards", ylab = "Passer Rating")
text(rushOff$Yds, passOff$Rate, labels = rushOff$Abv.)
cor(rushOff$Yds, passOff$Rate)## [1] -0.003510515plot(rushOff$Y.A, passOff$Yds, type = "n", xlab = "Rushing Y/A", ylab = "Passing Yards")
text(rushOff$Y.A, passOff$Yds, labels = rushOff$Abv.)
cor(rushOff$Y.A, passOff$Yds)## [1] -0.362417plot(rushOff$Yds, passOff$Yds, type = "n", xlab = "Rushing Yards", ylab = "Passing Yards")
text(rushOff$Yds, passOff$Yds, labels = rushOff$Abv.)
cor(rushOff$Yds, passOff$Yds)## [1] -0.5673019plot(passOff$Yds, rushOff$TD, type = "n", xlab = "Passing Yards", ylab = "Rushing TDs")
text(passOff$Yds, rushOff$TD, labels = rushOff$Abv.)
cor(passOff$Yds, rushOff$TD)## [1] 0.01427679